TWI776544B - Energy-saving control method and device for temperature control equipment - Google Patents

Abstract

An energy-saving control method for a temperature control device, wherein a building cooling and heating pattern prediction module of the energy-saving control device regulates and controls the temperature according to the location of a specific building, outdoor temperature/humidity, indoor temperature/humidity and the temperature in the specific building The operating state of the equipment, predicting a type of heating and cooling that the specific building belongs to, and the optimal temperature setting module of the energy-saving control device predicts the heating and cooling type and the specific building according to the cooling and heating type prediction module of the building. The current outdoor temperature/humidity of the object, a normal power consumption of the temperature control equipment installed in the specific building, determine an optimal set temperature with the best energy saving efficiency and output the optimal set temperature to the specific building The temperature control device in the object is operated at the optimal set temperature to reduce electricity consumption.

Description

Energy-saving control method and device for temperature control equipment

The present invention relates to a control method of a temperature regulation device, in particular to an energy-saving control method of a temperature regulation device capable of reducing electricity consumption.

In order to reduce the power consumption of indoor temperature control equipment (such as air-conditioning equipment and/or refrigeration or freezing equipment), a prior art is based on weather conditions and the corresponding power consumption when the temperature control equipment sets different temperatures under the weather conditions , establish an experience value database in a controller of the temperature control equipment, for example, when the outdoor temperature is 30C/humidity 62%, and the air conditioner is set at 24C, 25C, 26C, and 27C, the corresponding power consumption is 45 degrees. , 43 degrees, 41 degrees, 42 degrees. In this way, the controller of the air conditioner timely inquires the experience value database according to the weather information (for example, outdoor temperature 30C/humidity 60%), and learns that the set temperature with the least power consumption under this weather condition is 26C, that is, the control The air conditioner operates at 26C to reduce electricity consumption.

However, the power consumption of the temperature control equipment is not only affected by the above-mentioned weather conditions and the set temperature, but also the characteristics of the building where the temperature control equipment is located, such as whether the building is likely to become cold or hot due to weather changes, will also affect the temperature. Therefore, it is necessary to take the characteristics of the building as one of the considerations when setting the temperature of the temperature control equipment, so that the temperature control equipment can reduce the electricity consumption more effectively while regulating the temperature. .

Therefore, the purpose of the present invention is to provide an energy-saving control method and device for a temperature control device, which can comprehensively refer to the location of the building, the cold and hot characteristics, the operating state of the temperature control device, and the indoor and outdoor temperature/humidity parameters to control the temperature control device. The temperature control device operates at an optimal set temperature to reduce electricity consumption.

Therefore, the present invention is an energy-saving control method for a temperature control device, which is used to control a temperature control device installed in a specific building. The method is realized by an energy-saving control device for a temperature control device. The cold and heat type prediction module predicts that the specific building belongs to N (N is greater than 2 is a positive integer) one of the cooling and heating patterns; an optimal temperature setting module of the energy-saving control device predicts the cooling and heating pattern according to the building cooling and heating pattern prediction module and the current situation of the specific building The outdoor temperature/humidity of the specific building, a normal power consumption of the temperature control equipment in the specific building, determine an optimal set temperature with the best energy saving efficiency and output the optimal set temperature to the temperature in the specific building Regulating the device so that the temperature regulating device operates at the optimum set temperature.

In some embodiments of the present invention, the building cooling and heating pattern prediction module is obtained by a computer device using at least one building location, outdoor temperature/humidity historical data, indoor temperature/humidity historical data, and equipment. Train a neural network model on the operation history data of the temperature control equipment in the at least one building, so as to learn and establish the correlation between each of the buildings and the N types of heating and cooling, so that the building after the training is completed The cold and heat pattern prediction module can predict the specific building’s location according to the location of the specific building, the current outdoor temperature/humidity, the current indoor temperature/humidity, and the operating state of the temperature control equipment installed in the specific building. This kind of hot and cold type.

In some embodiments of the present invention, the optimal temperature setting module is obtained by a computer device using the historical data of the cooling and heating patterns, the outdoor temperature/humidity data of at least one building, and the information provided in the at least one building. A neural network model is trained based on the historical data of the electricity consumption of the temperature control equipment in the building, so that the historical data of the cooling and heating patterns, the outdoor temperature/humidity data and the temperature set in the at least one building are based on the historical data of the at least one building. The power consumption history data of the control equipment can be learned and found out under the conditions of various types of cold and heat, various outdoor temperature/humidity and various power consumption conditions, the temperature control equipment can set the most energy-saving (power-saving) temperature.

In some embodiments of the present invention, the energy-saving control device is a cloud server that communicates with the temperature control device through a network.

In some embodiments of the present invention, the energy-saving control device is disposed in the temperature control device, and communicates with the temperature control device through wired or wireless means.

The effect of the present invention lies in: predicting the specific building according to the location of the specific building, the outdoor temperature/humidity, the indoor temperature/humidity and the operation state of the temperature control equipment in the specific building by the building cooling and heating type prediction module Then the optimal temperature setting module predicts the cooling and heating pattern according to the building’s cooling and heating pattern prediction module, the current outdoor temperature/humidity of the specific building, the The usual power consumption of the temperature control equipment, controlling the temperature control equipment to operate at an optimal set temperature, can more accurately estimate the power consumption corresponding to the set temperature, so that the temperature control equipment can more accurately control the temperature and be more effective at the same time to reduce electricity consumption.

Before the present invention is described in detail, it should be noted that in the following description, similar elements are designated by the same reference numerals.

Referring to FIG. 1, it is the main flow of the energy-saving control method of the temperature control equipment of the present invention, which is used to control the temperature control equipment 11 (at least one or more temperature control equipment 11 (at least one or more temperature control equipment) arranged in a specific building 1 as shown in FIG. 2. Control equipment), the specific building 1 can be a general home, office building or store, etc., the temperature control equipment 11 can be cooling/heating air conditioning equipment and/or refrigeration or freezing equipment; and this embodiment is controlled by an energy-saving The device 2 is implemented, the energy-saving control device 2 can be a cloud or a remote server, and can communicate with the temperature control device 11 through a network (such as the Internet or the Internet of Things); the energy-saving control device 2 can also be a device At the proximal end, for example, a controller, a microcontroller or a microcomputer with computing capability is provided in the temperature control device 11 .

The energy-saving control device 2 mainly includes a building cooling and heating pattern prediction module 21 and an optimal temperature setting module 22 . These two modules can be loaded and executed by a processor in the energy-saving control device 2 A software program or a firmware that can be programmed or embedded in a processor of the energy saving control device 2, but not limited thereto.

In order to facilitate the understanding of this embodiment, the following description will be given by taking the temperature control device 11 being a cooling/heating air conditioner and the energy saving control device 2 being a cloud server as an example.

First, when the temperature control device 11 is in operation, the energy-saving control device 2 will control the temperature control device 11 to operate in a timely manner (for example, but not limited to, for example, every 1 hour, 2 hours, morning, afternoon, etc.) The optimum set temperature, therefore, when determining the optimum set temperature, the energy saving control device 2 will first obtain the location of the specific building 1, the current outdoor temperature/humidity, the current indoor temperature/humidity and the specific building Information such as the operating status (such as but not limited to the current set temperature) of the temperature control equipment 11 in the building 1, and the above information can be obtained from the temperature control equipment 11 and/or the indoor temperature of the specific building 1 A/hygrometer and an outdoor temperature/hygrometer are provided to the energy-saving control device 2 through the temperature control device 11 .

Then, as shown in step S1 in FIG. 1 , the building heating and cooling type prediction module 21 determines the location of the specific building 1 , the current outdoor temperature/humidity, the current indoor temperature/humidity, and the operating state of the temperature control device 11 . It is predicted which of the N (N is a positive integer greater than 1) cooling and heating types the specific building 11 belongs to. For example, the type of cooling and heating can be divided into, but not limited to, for example, the most easy-to-heat type, the easy-to-cool type, the hard-to-cool type, the easy-to-cool and hard-to-heat type, the easy-to-heat-hard-to-cool type, the most difficult to cool-to-heat type, etc.; The state prediction module 21 predicts that the specific building 11 belongs to one of these types of heating and cooling, such as the easy-to-cool type, and provides the predicted type of cooling and heating (easy-to-cool type) to the optimal temperature setting model. Group 22.

And in this embodiment, the building cooling and heating pattern prediction module 21 may be pre-collected by a computer device (not shown) using the location, outdoor temperature/humidity history of multiple (or at least one) buildings. data, indoor temperature/humidity historical data, and operation history data of the temperature control equipment installed in each of the buildings to train a neural network model (such as a deep neural network) to learn and establish each of the buildings and the N kinds of The correlation between the hot and cold types (such as the above-mentioned most easily cold and hot type, easy to cold and hot type, difficult to cold and hot type, easy to cold and not easy to heat type, easy to heat but not cold type, least cold and hot type); The building cooling and heating pattern prediction module 21 can be based on the location of the specific building 1, the current outdoor temperature/humidity, the current indoor temperature/humidity, and the operating state of the temperature control equipment 11 installed in the specific building 1, The type of heating and cooling to which the specific building 1 belongs is predicted.

Next, as shown in step S2 of FIG. 1 , the optimal temperature setting module 22 predicts the cooling and heating type (easy to cooling and heating type) according to the cooling and heating type prediction module 21 of the building and the current temperature of the specific building 1 . The outdoor temperature/humidity, a normal power consumption of the temperature control device 11 in the specific building 1, determine an optimal set temperature that can make the temperature control device 11 have the best energy saving efficiency, and output the optimal setting The temperature should be given to the temperature control device 11 so that the temperature control device 11 operates at the optimal set temperature to reduce power consumption. Wherein, the normal power consumption may refer to the average or median of the historical power consumption of the temperature control device 11 , such as daily or monthly normal power consumption.

And in this embodiment, the optimal temperature setting module 22 may be pre-collected by a computer device (not shown) using the historical data of the cooling and heating patterns of a plurality of (or at least one) buildings, and the outdoor temperature. / Humidity history data and electricity consumption history data of temperature control equipment located in each of the buildings (such as electricity consumption every 1 hour, every 2 hours, half day, whole day, etc.) to train a neural network model ( For example, a deep neural network), so that according to the historical data of the heating and cooling patterns of the buildings, the historical data of outdoor temperature/humidity and the historical data of the power consumption of the temperature control equipment in the buildings, learn and find the Under the conditions of hot and cold type, various outdoor temperature/humidity and various power consumption, how much temperature can be set by the temperature control device to save energy (power saving); thus, the optimal temperature setting module 22 after training can be set according to the input The heating and cooling type, the current outdoor temperature/humidity of the specific building, and the normal power consumption of the temperature control device 11 are calculated to calculate a Optimum set temperature.

For example, the optimal temperature setting module 22 calculates according to the current outdoor temperature/humidity and the normal power consumption when the cooling and heating type is the easy cooling and heating type, and finds that if the set temperature is 25C, the temperature adjustment The power consumption of the device 11 for one day will be 50 degrees. If the set temperature is 26C, the power consumption of the temperature control device 11 will be 40 degrees a day. If the set temperature is 27C, the temperature control device 11 will consume electricity for one day. The temperature will be 44 degrees, and the optimal temperature setting module 22 will select 26C as the optimal temperature setting.

To sum up, in the above embodiment, the building cooling and heating pattern prediction module 21 is used to control the operating state of the equipment 11 according to the location of the specific building 1 , the outdoor temperature/humidity, the indoor temperature/humidity, and the temperature in the specific building 1 . , predict a type of heating and cooling to which the specific building 1 belongs, and then the optimal temperature setting module 22 predicts the type of heating and cooling according to the cooling and heating type prediction module of the building and the current outdoor temperature of the specific building 1 /humidity, the usual power consumption of the temperature control device 11 in the specific building 1, determine an optimal set temperature and control the temperature control device 11 to operate at the optimal set temperature, and the location of the building and the heating and cooling As one of the considerations for controlling the set temperature of the temperature control equipment, the characteristic can more accurately estimate the power consumption corresponding to the set temperature, so that the temperature control equipment can control the temperature more accurately and reduce the power consumption more effectively. The effect and purpose of the present invention are indeed achieved.

However, the above are only examples of the present invention, and should not limit the scope of the present invention. Any simple equivalent changes and modifications made according to the scope of the application for patent of the present invention and the content of the patent specification are still within the scope of the present invention. within the scope of the invention patent.

1: specific buildings 11: Temperature control equipment 2: Energy saving control device 21: Building cooling and heating pattern prediction module 22: Optimal temperature setting module S1~S2: Steps

Other features and effects of the present invention will be clearly shown in the embodiments with reference to the drawings, wherein: Fig. 1 is the main flow of an embodiment of an energy-saving control method for a temperature control device of the present invention; and FIG. 2 is a schematic block diagram of modules included in an embodiment of the energy-saving control device of the temperature control device of the present invention.

S1~S2: Steps

Claims (10)

An energy-saving control method for temperature control equipment, used to control a temperature control equipment set in a specific building, the method comprising: (A) by a building cooling and heating pattern prediction module of an energy-saving control device according to the location of the specific building, outdoor temperature/humidity, indoor temperature/humidity and the operating state of the temperature control equipment in the specific building, It is predicted that the particular building belongs to one of N (N is a positive integer greater than 2) cooling and heating types; and (B) The cooling and heating pattern predicted by the building cooling and heating pattern prediction module and the current outdoor temperature/humidity of the specific building, the interior temperature of the specific building, and the A normal power consumption of the temperature control equipment determines an optimal set temperature with the best energy saving efficiency and outputs the optimal set temperature to the temperature control equipment in the specific building, so that the temperature control equipment operates in the Optimum set temperature. The energy-saving control method for temperature control equipment according to claim 1, wherein the building cooling and heating pattern prediction module is collected by a computer device using at least one building location, outdoor temperature/humidity historical data, indoor temperature/humidity The humidity history data and the operation history data of the temperature control equipment provided in the at least one building train a neural network model, so as to learn and establish the correlation between each of the buildings and the N kinds of cold and heat patterns, so that the training The completed building cooling and heating pattern prediction module can predict the specific building according to the location of the specific building, the current outdoor temperature/humidity, the current indoor temperature/humidity, and the operating state of the temperature control equipment installed in the specific building. A specific building belongs to one of the N types of cooling and heating. The energy-saving control method for a temperature control device as claimed in claim 1, wherein the optimal temperature setting module is obtained by a computer device using the historical data of the cooling and heating patterns, the outdoor temperature/humidity data, and the equipment of the at least one building collected. A neural network model is trained on the historical data of the power consumption of the temperature control equipment in the at least one building, so that according to the historical data of the cooling and heating type, the outdoor temperature/humidity data of the at least one building, and the data of the at least one building The historical data of the power consumption of the temperature control equipment in the building, learn and find out what temperature the temperature control equipment can set to save the most energy under various conditions of heating and cooling, various outdoor temperature/humidity and various power consumption conditions. The energy-saving control method for a temperature control device according to claim 1, wherein the energy-saving control device is a cloud server, which communicates with the temperature control device through a network. The energy-saving control method for a temperature control device as claimed in claim 1, wherein the energy-saving control device is disposed in the temperature control device and communicates with the temperature control device through wired or wireless means. An energy-saving control device for temperature control equipment is used to control a temperature control equipment set in a specific building, and includes: A building cooling and heating pattern prediction module, which predicts the specific building according to the location of the specific building, the current outdoor temperature/humidity, the current indoor temperature/humidity and the operating state of the temperature control equipment in the specific building Belonging to one of the N types of heat and cold; and An optimal temperature setting module, which is based on the cooling and heating pattern predicted by the building cooling and heating pattern prediction module, the current outdoor temperature/humidity of the specific building, and a temperature control device in the specific building. Usually electricity consumption determines an optimal set temperature with the best energy saving efficiency, and outputs the optimal set temperature to the temperature control equipment in the specific building, so that the temperature control equipment operates at the optimal set temperature. The energy-saving control device for temperature control equipment according to claim 6, wherein the building cooling and heating pattern prediction module is collected by a computer device using the location of at least one building, historical data of outdoor temperature/humidity, indoor temperature/humidity The humidity history data and the operation history data of the temperature control equipment provided in the at least one building train a neural network model, so as to learn and establish the correlation between each of the buildings and the N kinds of cold and heat patterns, so that the training The completed building cooling and heating pattern prediction module can predict the specific building according to the location of the specific building, the current outdoor temperature/humidity, the current indoor temperature/humidity, and the operating state of the temperature control equipment installed in the specific building. A specific building belongs to one of the N types of cooling and heating. The energy-saving control device for temperature control equipment as claimed in claim 6, wherein the optimal temperature setting module is obtained by a computer device using the historical data of the cooling and heating patterns, the outdoor temperature/humidity data, and the equipment of the at least one building collected. A neural network model is trained on the historical data of the power consumption of the temperature control equipment in the at least one building, so that according to the historical data of the cooling and heating type, the outdoor temperature/humidity data of the at least one building, and the data of the at least one building The historical data of the power consumption of the temperature control equipment in the building, learn and find out what temperature the temperature control equipment can set to save the most energy under various conditions of heating and cooling, various outdoor temperature/humidity and various power consumption conditions. The energy-saving control device for a temperature control device according to claim 6, wherein the energy-saving control device is a cloud server that communicates with the temperature control device through a network. The energy-saving control device for a temperature control device as claimed in claim 6, wherein the energy-saving control device is disposed in the temperature control device and communicates with the temperature control device through wired or wireless means.

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